Psychother Psychosom Med Psychol. Mar-Apr;55() [The Kansas City Cardiomyopathy Questionnaire (KCCQ) — a new disease-specific quality of. Background. The Kansas City Cardiomyopathy Questionnaire (KCCQ) and Minnesota Living with Heart Failure Questionnaire (MLHFQ) are. The Kansas City. Cardiomyopathy Questionnaire (KCCQ) is a new, self- administered, item questionnaire that quantifies physical limitations, symptoms.

Author: Fegul Mezikinos
Country: Cyprus
Language: English (Spanish)
Genre: Politics
Published (Last): 22 July 2007
Pages: 321
PDF File Size: 5.44 Mb
ePub File Size: 19.71 Mb
ISBN: 935-4-11877-255-4
Downloads: 69215
Price: Free* [*Free Regsitration Required]
Uploader: Vikus

To receive news and publication updates for Cardiology Research and Practice, enter your email address in the box below. This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Heart failure HF is one of the most common diagnoses associated with hospital readmission. We designed this prospective study to evaluate whether Kansas City Cardiomyopathy Questionnaire KCCQ score is associated with day readmission in patients hospitalized with decompensated HF. We enrolled patients who met the study criteria. Compared to readmitted patients, nonreadmitted patients had a higher average KCCQ score The combination of home medication and lab tests on the base model resulted in an integrated discrimination improvement IDI increase of 3.

The KCCQ score determined before hospital discharge was significantly associated with day readmission rate in patients with HF, which may provide a clinically useful measure and could significantly improve readmission prediction reliability when combined with other clinical components.

It is estimated that heart failure HF affects over 5. Readmission of HF after hospitalization is common, and unfortunately many of these readmissions are predictable and possibly preventable [ 23 ].

Although new data showed reduction in Medicare hospital readmission rates [ 4 ], HF is still one of the most common diagnoses associated with day readmission; an analysis of to Medicare claims-based data showed that These concerning statistics paved the way for a stronger focus on tools to predict and prevent such readmissions.

This questionnaire identified the following clinically relevant domains: Previous studies have shown that KCCQ score correlated with survival and hospitalization in patients with HF [ 7 ] and was an independent predictor of poor prognosis in this patient population [ 8 ]. In addition, KCCQ score measured 1 week after hospital discharge independently predicted one-year survival free of cardiovascular readmission [ 9 ]. More recently, KCCQ has also been studied during acute HF hospitalization and demonstrated sensitivity to acute changes, but score changes during hospitalization did not predict short-term readmission [ 10 ], although it was a relatively small study, with a sample size of only 52 patients, and it did not investigate the relationship between KCCQ score and HF readmission.

Therefore, whether KCCQ score can be used to predict the short-term readmission has yet to be completely evaluated.

The Kansas City Cardiomyopathy Questionnaire (KCCQ)

To address these gaps in knowledge and explore the feasibility of using the KCCQ score to predict the short-term HF readmission, we designed and conducted this prospective study. The study was approved by the Florida Hospital Institutional Review Board and conducted in accordance with the Declaration of Helsinki.

The study was conducted at Florida Hospital, Orlando Campus. Patients who were admitted to the HF unit were screened and enrolled for the study. Exclusion criteria were noncardiac disease with a life expectancy of less than one year, HF due to uncorrected valvular heart disease, lccq illness interfering with an appropriate follow-up, inability to understand study procedure, and inability to provide informed consent.

Primary endpoint was day readmission carsiomyopathy and the KCCQ score. Admission comorbid conditions, demographics, laboratory, echocardiographic data, and medications on discharge were secondary endpoints. For kcq patient who met the study criteria, a trained research assistant explained the study to the patient and administered the KCCQ after a written informed consent was obtained.

The Kansas City Cardiomyopathy Questionnaire

The assessment was generally completed within 1—3 days before discharge. A follow-up conversation was performed over the telephone 30 days after discharge to determine if rehospitalization occurred or not.

Postdischarge readmission information was gathered through follow-up interview with the patient. To evaluate associations between KCCQ score and readmission within 30 days after discharge, we first compared the difference between the nonreadmission group and readmission group in terms of the KCCQ scores, demographic characteristics, comorbidity, medications, and laboratory data using univariate analysis. We then performed multivariate analysis to investigate how each clinical factor was associated with HF readmissions after controlling for the other factors.

In the multivariate analysis, logistic regression models were used, and adjusted odds ratios OR were estimated for each factor hypothesized to predict HF readmission. We included HF readmission as a dependent variable and all potential factors as independent predictors in the logistic regression irrespective of whether they showed a significant difference between readmission citty nonreadmission groups in the univariate analysis.


After the multivariate analysis, we further constructed five simplified prediction models and evaluated the importance of KCCQ score in the final model through comparing area under receiver operating characteristic curve ROC of each model. In this analysis, we also used integrated discrimination improvement IDIdescribed by Pencina et al. As no nested missing pattern was detected, multiple imputation models were used for data imputation. As age was a continuous variable and race was a binary variable, normal linear regression was used for age while logistic regression was used for race imputation.

All analyses were performed by Stata version 14 StataCorp. All values were two-tailed, and was set as the level of statistical significance for all tests. In total, patients were enrolled in the study. There was no significant difference between the nonreadmitted and readmitted patients in terms of average age However, a significant difference between these two groups was noted on comparing gender, with male patients being more prone to being readmitted than female None of the comorbidities showed significant difference in the relative frequency between the readmission and nonreadmission group Table 1.

The KCCQ score, lab test results on admission, and discharge medications kansqs compared between cardiomopathy nonreadmitted and readmitted patients Table 2. The average KCCQ score was significantly higher in the nonreadmitted patients than in readmitted patients Compared to readmitted patients, nonreadmitted cardiomyopathhy had a higher ejection fraction on admission However, no significant difference was detected on comparing discharge medications, blood sodium level, or HGB between the two groups of patients in the univariate analysis Table 2.

To further investigate the effect of each independent variable while controlling other covariates, multivariate analyses were performed Table 3 and Figure questiknnaire.

The Kansas City Cardiomyopathy Questionnaire (KCCQ)

One possible interpretation could be that patients who have had a myocardial infarction are more likely to have wall motion abnormalities and fixed myocardial defects and thus a lower ejection fraction than those with nonobstructive coronary artery disease without an MI, leading to opposite contribution to HF readmission.

In order to evaluate how much contribution the KCCQ score made in predicting HF readmission, we developed a model by including seven factors besides KCCQ score model 5 based on the multivariate regression results, published literature, and models.

The c -statistic indicated that model 5 which included KCCQ score and all other potential predictors had the highest c -statistic value 0. As seen in Table 4the IDI analysis demonstrated that the discriminatory performance of model 5 improved by 6. These results suggested that the KCCQ score, as a single independent variable, is one of the important factors that could potentially be used for predicting readmission rates of HF patients within 30 days after discharge, and a combination of all these important factors would offer the greatest incremental gain.

In this prospective study, we found that the KCCQ score was significantly associated with short-term HF readmission rate. It contributed to improving the c -statistics of a model based on age, gender, medications, laboratory data, and LVEF available at discharge from 0. These findings may provide some help to guide follow-up strategies towards delivering optimal care, such as encouraging patients with lower KCCQ to have an early follow-up [ 14 ].

Lots of efforts have been made to identify the predictable factors that are associated with high risk of being readmitted, which has been quite challenging until now. In this study, we found that HF patients who had lower KCCQ score at time of discharge and lower EF and of male gender seemed to be more prone for readmission within 30 days. These findings were similar to some studies but not others.

As a matter of fact, no specific patient or hospital factors have been shown to consistently predict day readmission after hospitalization for HF. In a systematic review of studies describing the association between traditional patient characteristics and readmission after hospitalization for HF, left ventricular EF, as well as other factors such as demographic characteristics, comorbid conditions, and New York Heart Association class, was associated with readmission in only a minority of cases [ 13 ].

In another meta-analysis of 69 studies and factors for short-term readmission, noncardiovascular comorbidities, poor physical condition, history of admission, and failure to use evidence-based medication, rather than cardiovascular comorbidities, age, or gender, were more strongly associated with short-term readmission [ 15 ].


More recently, KCCQ score was used to assess the feasibility of reflecting the changes of acute HF during hospitalization and predicting day readmission. The authors found that it was feasible to use the KCCQ during acute HF hospitalizations and was sensitive to clinical improvement, but score changes during hospitalization did not predict day readmission. However, this study was a relatively small study that included only 54 patients and was focused on KCCQ score differences during hospitalization between nonreadmission and admission groups [ 10 ].

In contrast, more than patients were enrolled in our study and the KCCQ score was higher in nonreadmitted HF patients and was independently associated with lower day readmission. As mentioned above, there are multiple factors contributing to HF readmission; therefore, risk prediction models including and weighing all relevant factors were developed.

In these models, discrimination, defined by the area under the receiver operating characteristic ROC curve, is used to tell how well a model can separate those who will have the outcome from those who will not have the outcome of interest.

In this case, if the predicted risks for readmitted patients are all higher than for patients who are not readmitted, the model discriminates perfectly with c -statistic of 1. Conversely, if risk prediction is no better than chance, the c -statistic is 0. Models are typically considered reasonable when the c -statistic is greater than 0. For day readmission after HF hospitalization, several models have been developed.

Only two models have generated c -statistics greater than 0. One of them is the automated model developed by Amarasingham et al. The other model combined claims-based demographic and comorbidity data with clinical data including vital signs, laboratory values, and measured left ventricular ejection fraction [ 18 ]. However, neither of the two models included KCCQ scores. Given only 48 readmissions in our study population, we included only 7 parameters besides the KCCQ score in the full model model 5.

The full model model 5which included the KCCQ score, increased the c -statistics of 0. Given that many other possible risk factors have not been included in this model, such as GFR and BNP, this model may not be perfect, although its c -statistics was greater than 0. This study was performed in a single-community medical center, and further studies in other centers or multiple centers need to be done to validate our findings.

We only administered the KCCQ one time during the hospitalization, which would not reflect changes between admission, during hospitalization, and after hospitalization.

We did not collect some relevant medical history, such as history of admission due to heart failure in the past; physical examination findings; some other labs such as GFR and BNP, or chest X-ray findings.

These factors could also be important in the risk prediction model. Competency in Medical Knowledge. Heart failure is one of the most common diagnoses associated with readmission. KCCQ score provided important prognostic information for predicting day readmission and it can significantly improve prediction reliability along with other critical components. Additional clinical studies need to be done in multiple centers with a larger sample size to validate our finding. Future research should include relevant physical examination findings and chest X-ray findings, which could be important in the risk prediction model.

The authors wish to acknowledge the following participating doctors from Florida Hospital Orlando who helped with data collection: Cardiology Research and Practice. Indexed in Science Citation Index Expanded.

Subscribe to Table of Contents Alerts. Table of Contents Alerts. Introduction It is estimated that heart failure HF affects over 5. Results In total, patients were enrolled in the study. Summary of demographic characteristics and medical history between HF readmission and nonreadmission within 30 days after discharge. Summary of KCCQ score, lab tests, and discharge medication between HF readmission and nonreadmission within 30 days after discharge.

Summary of multivariate analysis investigating the effects of demographic characteristics, medical history, discharge medication, lab test, and overall KCCQ score on readmission rate within 30 days after discharge. Adjusted odds ratios of readmission within 30 days after discharge derived from multivariate logistic regression analysis. Prognostic questtionnaire of readmission within 30 days after discharge of different models comparing to model 1 with only demographic predictors.